Applied Machine Learning and Deep Learning with R [Video]

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Applied Machine Learning and Deep Learning with R [Video]

Olgun Aydin

Learn to build powerful machine learning and deep learning applications with help of the R programming language and its various packages

Quick links: > What will you learn?> Table of content

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Video Details

ISBN 139781788394673
Course Length2 hours and 15 minutes

Video Description

In this course, we will examine in detail the R software, which is the most popular statistical programming language of recent years.

You will start with exploring different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, you will dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, you will learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. The elements of deep learning neural networks, types of deep learning networks, frameworks used for deep learning applications will be addressed and applications will be done with R TensorFlow package. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR.

Style and Approach

A step-by-step real world guide on machine learning and deep learning that takes you through the core aspects for building powerful data science applications with the help of the R programming language

Table of Contents

Introduction to Machine Learning
The Course Overview
Supervised and Unsupervised Learning
Feature Selection
Model Evaluation Methods – Cross Validation
Performance Metrics
Clustering
K-Means Clustering
Hierarchical Clustering
DBSCAN Algorithm
Clustering Exercises with R
Dealing with Problems About Clustering
Classification
k-NN Classification
Logistic Regression
Naive Bayes
Decision Trees
Classification Exercises with R
Handling Problems About Classification
Artificial Neural Networks
Introduction to Artificial Neural Networks
Types of Artificial Neural Networks
Back Propagation
Artificial Neural Networks Exercises with R
Tricks for ANN in R
Introduction to Deep Learning
What Is Deep Learning?
Elements of Deep Neural Networks
Types of Deep Neural Networks
Introduction to Deep Learning Frameworks
Exercises with TensorFlow in R
Tricks About Application of Deep Neural Nets
Machine Learning with SparkR
Introduction to SparkR
Installation of SparkR
Writing First Script on SparkR
Generalized Linear Models with SparkR
Classification Exercises with SparkR
Clustering Exercises with SparkR
Naive Bayes with SparkR
Tricks About SparkR

What You Will Learn

  • Classify data with the help of statistical methods such as k-NN Classification, Logistic Regression, and Decision Trees
  • Deal with imbalanced datasets in artificial neural networks
  • Deep learning algorithms Tensorflow background in R
  • Write machine learning scripts with SparkR

Authors

Table of Contents

Introduction to Machine Learning
The Course Overview
Supervised and Unsupervised Learning
Feature Selection
Model Evaluation Methods – Cross Validation
Performance Metrics
Clustering
K-Means Clustering
Hierarchical Clustering
DBSCAN Algorithm
Clustering Exercises with R
Dealing with Problems About Clustering
Classification
k-NN Classification
Logistic Regression
Naive Bayes
Decision Trees
Classification Exercises with R
Handling Problems About Classification
Artificial Neural Networks
Introduction to Artificial Neural Networks
Types of Artificial Neural Networks
Back Propagation
Artificial Neural Networks Exercises with R
Tricks for ANN in R
Introduction to Deep Learning
What Is Deep Learning?
Elements of Deep Neural Networks
Types of Deep Neural Networks
Introduction to Deep Learning Frameworks
Exercises with TensorFlow in R
Tricks About Application of Deep Neural Nets
Machine Learning with SparkR
Introduction to SparkR
Installation of SparkR
Writing First Script on SparkR
Generalized Linear Models with SparkR
Classification Exercises with SparkR
Clustering Exercises with SparkR
Naive Bayes with SparkR
Tricks About SparkR

Video Details

ISBN 139781788394673
Course Length2 hours and 15 minutes
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